Regarding to the computer system security, the intrusion detection systems are fundamental components for discriminating attacks at the early stage. They monitor and analyze network traffics, looking for abnormal behaviors or attack signatures to detect intrusions in early time. However, many challenges arise while developing flexible and efficient network intrusion detection system (NIDS) for unforeseen attacks with high detection rate. In this paper, deep neural network (DNN) approach was proposed for anomaly detection NIDS. Dropout is the regularized technique used with DNN model to reduce the overfitting. The experimental results applied on NSL_KDD dataset. SoftMax output layer has been used with cross entropy loss function to enforce the proposed model in multiple classification, including five labels, one is normal and four others are attacks (Dos, R2L, U2L and Probe). Accuracy metric was used to evaluate the model performance. The proposed model accuracy achieved to 99.45%. Commonly the recognition time is reduced in the NIDS by using feature selection technique. The proposed DNN classifier implemented with feature selection algorithm, and obtained on accuracy reached to 99.27%.
The majority of Arab EFL (English as a Foreign Language) learners struggle with speaking English fluency. Iraqi students struggle to speak English confidently due to mispronunciation, grammatical errors, short and long pauses while speaking or feeling confused in normal conversations. Collaborative learning is crucial to enhance student’s speaking skills in the long run. This study aims to state the importance of collaborative learning as a teaching method to EFL learners in the meantime. In this quantitative and qualitative study, specific focus is taken on some of Barros’s views of collaborative learning as a teamwork and some of Pattanpichet’s speaking achievements under four categories: academic benefits, social benefits,
... Show MoreArtificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le
... Show MoreThe intellectual property of digital documents has been protected by using many methods of digital watermarking. Digital documents have been so much of advantages over print documents. Digital documents are less expensive and easy to store, transport, and searched compared to traditional print documents. But it has its owner limitation too. A simple image editor can be used to modify and make a forged document. Digital documents can be tampered easily. In order to utilize the whole benefits of digital document, these limitations have to overcome these limitations by embedding some text, logo sequence that identifies the owner of the document..
In this research LSB technique has been used
... Show MoreThe DEM (Digital elevation model) means that the topography of the earth's surface (such as; Terrain relief and ocean floors), can be described mathematically by elevations as functions of three positions either in geographical coordinates, (Lat. Long. System) or in rectangular coordinates systems (X, Y, Z). Therefore, a DEM is an array number that represents spatial distributions of terrain characteristics. In this paper, the contour lines with different interval of high-resolution digital elevation model (1m) for AL-khamisah, The Qar Government was obtained. The altitudes ranging is between 1 m – 8.5 m, so characterized by varying heights within a small spatial region because it represents in multiple spots with flat surfaces.
This paper describes the application of consensus optimization for Wireless Sensor Network (WSN) system. Consensus algorithm is usually conducted within a certain number of iterations for a given graph topology. Nevertheless, the best Number of Iterations (NOI) to reach consensus is varied in accordance with any change in number of nodes or other parameters of . graph topology. As a result, a time consuming trial and error procedure will necessary be applied
to obtain best NOI. The implementation of an intellig ent optimization can effectively help to get the optimal NOI. The performance of the consensus algorithm has considerably been improved by the inclusion of Particle Swarm Optimization (PSO). As a case s
This paper examines the change in planning pattern In Lebanon, which relies on vehicles as a semi-single mode of transport, and directing it towards re-shaping the city and introducing concepts of "smooth or flexible" mobility in its schemes; the concept of a "compact city" with an infrastructure based on a flexible mobility culture. Taking into consideration environmental, economical and health risks of the existing model, the paper focuses on the four foundations of the concepts of "city based on culture flexible mobility, "and provides a SWOT analysis to encourage for a shift in the planning methodology.
The paper uses the Direct Synthesis (DS) method for tuning the Proportional Integral Derivative (PID) controller for controlling the DC servo motor. Two algorithms are presented for enhancing the performance of the suggested PID controller. These algorithms are Back-Propagation Neural Network and Particle Swarm Optimization (PSO). The performance and characteristics of DC servo motor are explained. The simulation results that obtained by using Matlab program show that the steady state error is eliminated with shorter adjusted time when using these algorithms with PID controller. A comparative between the two algorithms are described in this paper to show their effectiveness, which is found that the PSO algorithm gives be
... Show Moreهدفت هذه الدراسة إلى التعرف على دور نظام معلومات الموارد البشرية في تحقيق متطلبات عملية تقويم أداء أعضاء الهيئة التدريسية في كلية الإدارة والاقتصاد/جامعة بغداد. وقد تحددت مشكلة الدراسة في عدد من التساؤلات، أهمها: ما تأثير كل متغير من متغيرات نظام معلومات الموارد البشرية في عملية تقويم أداء أعضاء الهيئة التدريسية في الكلية المبحوثة؟ وما تأثير متغيرات نظام معلومات الموارد البشرية مجتمعة في عملية تقويم الأداء
... Show MoreThe Ant System Algorithm (ASA) is a member of the ant colony algorithms family in swarm intelligence methods (part of the Artificial Intelligence field), which is based on the behavior of ants seeking a path and a source of food in their colonies. The aim of This algorithm is to search for an optimal solution for Combinational Optimization Problems (COP) for which is extremely difficult to find solution using the classical methods like linear and non-linear programming methods.
The Ant System Algorithm was used in the management of water resources field in Iraq, specifically for Haditha dam which is one of the most important dams in Iraq. The target is to find out an efficient management system for
... Show MoreIn this paper, the homotopy perturbation method (HPM) is presented for treating a linear system of second-kind mixed Volterra-Fredholm integral equations. The method is based on constructing the series whose summation is the solution of the considered system. Convergence of constructed series is discussed and its proof is given; also, the error estimation is obtained. Algorithm is suggested and applied on several examples and the results are computed by using MATLAB (R2015a). To show the accuracy of the results and the effectiveness of the method, the approximate solutions of some examples are compared with the exact solution by computing the absolute errors.